​​GANs from Scratch 1: A deep introduction.

Great introduction and tutorial. With code in PyTorch and TensorFlow

Link: https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f

#tensorflow #pytorch #GAN #tutorial #entrylevel #novice #wheretostart

🎓 Reinforcement Learning Course from OpenAI

Reinforcement Learning becoming significant part of the data scientist toolbox.
OpenAI created and published one of the best courses in #RL. Algorithms implementation written in #Tensorflow.
But if you are more comfortable with #PyTorch, we have found #PyTorch implementation of this algs

OpenAI Course: https://spinningup.openai.com/en/latest/
Tensorflow Code: https://github.com/openai/spinningup
PyTorch Code: https://github.com/kashif/firedup

#MOOC #edu #course #OpenAI

Applying deep learning and Tensorflow to improve brain MRI images quality

Taking brain MRI images is complicated procedure as the orientation, location, and coverage needs to be correct in all three spatial dimentsions. The quality and consistency of positioning and orientation of the slices relies heavily on the skill and experience of the scan operator. This process can be time-consuming and difficult, especially for complex anatomies. As a result, there can be inconsistencies from scan operator to scan operator. This lack of consistency can make the job of the radiologist in interpreting these images more difficult especially when a patient is being scanned as a follow up to previous MRI exam and they are trying to identify subtle changes in anatomy or disease progression over time.

The researchers from GE Healthcare Magnetic Resonance Imaging team developed an approach to aid the scan operator. The approach is based on 3 deep neural networks, can be adopted to take MRI images of the other body parts and achieves 99.2% accuracy score. The researchers notice that Tensorflow significantly helped them to develop and deliver the approach to the production.

Medium article: https://medium.com/tensorflow/intelligent-scanning-using-deep-learning-for-mri-36dd620882c4
GE Helthcare website: https://www.gehealthcare.com

#Tensorflow #medicine #casestudy #DL #CV

With new TensorBoard.dev you can share your DL/ML experiments result at tensorBoard

Link: https://blog.tensorflow.org/2019/12/introducing-tensorboarddev-new-way-to.html

#DL #ML #tensorflow #tf

​​GAN Lab
Understanding Complex Deep Generative Models using Interactive Visual Experimentation

#GAN Lab is a novel interactive visualization tool for anyone to learn & experiment with Generative Adversarial Networks (GANs), a popular class of complex #DL models. With GAN Lab, you can interactively train GAN models for #2D data #distributions and visualize their inner-workings, similar to #TensorFlow Playground.

web-page: https://poloclub.github.io/ganlab/
github: https://github.com/poloclub/ganlab
paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf

​​TensorFlow Quantum
A Software Framework for Quantum Machine Learning

Introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
TFQ provides the tools necessary for bringing the quantum computing and ML research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50-100 qubits.

A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced – quantum data and hybrid quantum-classical models.

Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. Quantum data, which can be generated/simulated on quantum processors/sensors/networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.

Quantum models cannot use quantum processors alone – NISQ processors will need to work in concert with classical processors to become effective. As TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is a natural platform for experimenting with hybrid quantum-classical algorithms.

To build and train such a model, the researcher can do the following:
– prepare a quantum dataset
– evaluate a quantum NN model
- sample or Average
– evaluate a classical NN model
– evaluate сost function
– evaluate gradients & update parameters


blog post: https://ai.googleblog.com/2020/03/announcing-tensorflow-quantum-open.html
paper: https://arxiv.org/abs/2003.02989

#tfq #tensorflow #quantum #physics #ml

​​train your tf models on google cloud by tensorflow cloud

tf cloud is a python package that provides api for a transition from debugging and training keras & tf code in the local environment to distributed training in google cloud. it simplifies the process of training models on the cloud into a single, simple function call, requiring minimal setup and almost zero changes to model.
tf cloud handles cloud-specific tasks such as creating vm instances and distribution strategies for models automatically.


blog post: https://blog.tensorflow.org/2020/08/train-your-tensorflow-model-on-google.html?linkId=95907203
github: https://github.com/tensorflow/cloud

#tensorflow #cloud

​​Language-agnostic BERT Sentence Embedding

Authors adopt multilingual BERT to produce language-agnostic sentence embeddings for 109 languages.
The model combines a masked language model (MLM) and a translation language model (TLM) pretraining with a translation ranking task using bi-directional dual encoders.
The resulting multilingual sentence embeddings improve average bi-text retrieval accuracy over 112 languages to 83.7% on Tatoeba (previous state-of-the-art was 65.5%)

blogpost: https://ai.googleblog.com/2020/08/language-agnostic-bert-sentence.html
paper: https://arxiv.org/abs/2007.01852
bodel on tf hub: https://tfhub.dev/google/LaBSE/1

#deeplearning #transformers #nlp #tensorflow #sentenceembeddings

Lo-Fi Player

The team from the magenta project, that does research about deep learning and music powered by TensorFlow in Google, obviously, release a new fun project lofi-player powered by their open-source library magenta.js.

So it's basically a lo-fi music generator which popular genre on youtube streams and other kinds of stuff. You can customize the vibe on your manner and wish from sad to moody, slow to fast, etc.

It is based on their earlier work MusicVae to sample latent space of music and MelodyRNN to generate music sequences from different instruments. The project is not about new research, but to show what can do with an already done library in a creative way.

They also create a stream on youtube to listen lo-fi generated by that application and users in chat can together tune lo-fi player in stream :)

#magenta #lo-fi #music #google #tensorflow #fun

​​Tutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow

Nice tutorial with enough theory to understand what you are doing and code to get it done.

Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/

#Keras #TensorFlow #tutorial #wheretostart #GAN